(ii) Independence from Network Infrastructure: Traditionally, sensor data is offloaded onto models running on smartphone-class edge devices or cloud servers [1]}[2]}. This is not suitable for time-critical sense-compute-actuation applications such as autonomous driving [3]}[4]}, robot control [5]}[6]}, and industrial control system. Moreover, reliable network bandwidth or power may not be available for communicating with online models, such as in wildlife monitoring [7]} or energy-harvesting intermittent systems [8]}[9]}[10]}. TinyML allows offline and on-board inference without requiring data offloading or cloud-based inference.
